# Copyright (c) 2025 Huawei Technologies Co., Ltd.
# This program is free software, you can redistribute it and/or modify it under the terms and conditions of
# CANN Open Software License Agreement Version 2.0 (the "License").
# Please refer to the License for details. You may not use this file except in compliance with the License.
# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED,
# INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
# See LICENSE in the root of the software repository for the full text of the License.

import pytest

import asc
import asc.runtime.config as config
import asc.lib.runtime as rt
from asc.language.core.dtype import KnownTypes as KT
from asc.language.core.struct import Field, Struct, StructField

try:
    import torch
except ModuleNotFoundError:
    pytest.skip("torch is not installed", allow_module_level=True)


class BlockConfig(Struct):
    block_length = Field(dtype=KT.int32, default=0)


class TileConfig(Struct):
    tile_length = Field(dtype=KT.int32, default=0)
    tile_num = Field(dtype=KT.int32, default=0)


class KernelConfig(Struct):
    block = StructField(BlockConfig)
    tile = StructField(TileConfig)


@asc.jit
def vadd_kernel(x: asc.GlobalAddress, y: asc.GlobalAddress, z: asc.GlobalAddress, buffer_num: asc.ConstExpr[int],
                kernel_config: KernelConfig):
    offset = asc.get_block_idx() * kernel_config.block.block_length
    x_gm = asc.GlobalTensor()
    y_gm = asc.GlobalTensor()
    z_gm = asc.GlobalTensor()
    x_gm.set_global_buffer(x + offset)
    y_gm.set_global_buffer(y + offset)
    z_gm.set_global_buffer(z + offset)
    pipe = asc.TPipe()
    in_queue_x = asc.TQue(asc.TPosition.VECIN, 1)
    in_queue_y = asc.TQue(asc.TPosition.VECIN, 1)
    out_queue_z = asc.TQue(asc.TPosition.VECOUT, 1)
    pipe.init_buffer(que=in_queue_x, num=buffer_num, len=kernel_config.tile.tile_length * x.dtype.sizeof())
    pipe.init_buffer(que=in_queue_y, num=buffer_num, len=kernel_config.tile.tile_length * y.dtype.sizeof())
    pipe.init_buffer(que=out_queue_z, num=buffer_num, len=kernel_config.tile.tile_length * z.dtype.sizeof())
    for i in range(kernel_config.tile.tile_num):
        copy_in(i, x_gm, y_gm, in_queue_x, in_queue_y, kernel_config.tile.tile_length)
        compute(z_gm, in_queue_x, in_queue_y, out_queue_z, kernel_config.tile.tile_length)
        copy_out(i, z_gm, out_queue_z, kernel_config.tile.tile_length)


@asc.jit
def copy_in(i: int, x_gm: asc.GlobalAddress, y_gm: asc.GlobalAddress, in_queue_x: asc.TQue, in_queue_y: asc.TQue,
            tile_length: int):
    x_local = in_queue_x.alloc_tensor(x_gm.dtype)
    y_local = in_queue_y.alloc_tensor(y_gm.dtype)
    asc.data_copy(x_local, x_gm[i * tile_length:], count=tile_length)
    asc.data_copy(y_local, y_gm[i * tile_length:], count=tile_length)
    in_queue_x.enque(x_local)
    in_queue_y.enque(y_local)


@asc.jit
def compute(z_gm: asc.GlobalTensor, in_queue_x: asc.TQue, in_queue_y: asc.TQue, out_queue_z: asc.TQue,
            tile_length: int):
    # "z_gm" is passed here to obtain dtype
    x_local = in_queue_x.deque(z_gm.dtype)
    y_local = in_queue_y.deque(z_gm.dtype)
    z_local = out_queue_z.alloc_tensor(z_gm.dtype)
    asc.add(z_local, x_local, y_local, tile_length)
    out_queue_z.enque(z_local)
    in_queue_x.free_tensor(x_local)
    in_queue_y.free_tensor(y_local)


@asc.jit
def copy_out(i: int, z_gm: asc.GlobalTensor, out_queue_z: asc.TQue, tile_length: int):
    z_local = out_queue_z.deque(z_gm.dtype)
    asc.data_copy(z_gm[i * tile_length:], z_local, count=tile_length)
    out_queue_z.free_tensor(z_local)


def vadd_launch(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
    assert x.shape == y.shape
    assert x.dtype == y.dtype
    z = torch.zeros_like(x)
    total_length = z.numel()
    use_core_num = 16
    block_length = (total_length + use_core_num - 1) // use_core_num
    tile_length = 512
    tile_num = (block_length + tile_length - 1) // tile_length
    buffer_num = 1
    block_config = BlockConfig(block_length=block_length)
    tile_config = TileConfig(tile_length=tile_length, tile_num=0)
    tile_config.tile_num = tile_num
    kernel_config = KernelConfig(tile=tile_config)
    kernel_config.block = block_config
    vadd_kernel[use_core_num, rt.current_stream()](x, y, z, buffer_num, kernel_config)
    return z


param_list = [
    [torch.float32, (1000,)],
    [torch.float32, (1,)],
    [torch.float32, (9999,)],
    [torch.float16, (2048,)],
    [torch.int32, (8192,)],
    [torch.int16, (8192,)],
    [torch.int32, (153, 834)],
]


BACKENDS = [
    # config.Backend.Model,
    config.Backend.NPU,
]


@pytest.mark.parametrize("dtype, size", param_list)
@pytest.mark.parametrize("backend", BACKENDS)
def test_vadd_tiling(dtype, size, backend: config.Backend):
    config.set_platform(backend)
    device = "npu" if config.Backend(backend) == config.Backend.NPU else "cpu"
    if dtype in {torch.float16, torch.float32}:
        x = torch.randn(size, dtype=dtype, device=device)
        y = torch.randn(size, dtype=dtype, device=device)
    else:
        x = torch.randint(-100, 99, size, dtype=dtype, device=device)
        y = torch.randint(-100, 99, size, dtype=dtype, device=device)
    z = vadd_launch(x, y)
    assert torch.allclose(z, x + y)
